Optical coherence tomography is a noninvasive, noncontact imaging modality that uses coherence gating to obtain high-resolution cross-sectional images of tissue microstructure. In Fourier domain OCT (FD-OCT), the interferometric signal between light from a reference and the back-scattered light from a sample point is recorded in the frequency domain rather than the time domain. After a wavelength calibration, a one-dimensional Fourier transform is taken to obtain an A-line spatial distribution of the object scattering potential. The spectral information discrimination in FD-OCT can be accomplished by using a dispersive spectrometer in the detection arm in the case of spectral-domain OCT (SD-OCT) or rapidly tuning a swept laser source in the case of swept-source OCT (SS-OCT).
Recently there has been a lot of interest in using intensity based and phase-sensitive based OCT techniques, collectively named OCT Angiography, to map the retinal vasculature or identify regions with flow in the tissue (see for example An et al. “Optical microangiography provides correlation between microstructure and microvasculature of optic nerve head in human subjects,” J. Biomed. Opt. 17, 116018 (2012), Zhao et al., “Doppler standard deviation imaging for clinical monitoring of in vivo human skin blood flow,” Optics Letters 25, 1358-1360 (2000), Fingler et al. “Mobility and transverse flow visualization using phase variance contrast with spectral domain optical coherence tomography” Optics Express. Vol. 15, No. 20. pp 12637-12653 (2007), Makita et al., “Optical Coherence Angiography,” Optics Express, 14(17), 7821-7840 (2006), Mariampillai et al., “Optimized speckle variance OCT imaging of microvasculature,” Optics Letters 35, 1257-1259 (2010), and Wang et al., “Frequency domain phase-resolved optical Doppler and Doppler variance tomography” Optics Communications 242 345-350 (2004) hereby incorporated by reference). OCT Angiography provides a non-invasive technique to visualize and indirectly quantify the integrity of retinal circulation pathways. Anomalies in retinal circulation have a direct relation to ocular pathologies, especially within the macula, wherein compromised hemo-dynamics may not only be related to decreased visual acuity, but could also be a surrogate biomarker for ocular pathologies like retinal vein occlusion (RVO), diabetic retinopathy (DR), and intra retinal microvasculature abnormality (IRMA). Specifically, correlation between retinal vasculature and blood flow are attributes of interest in a number of ocular defects. DR and RVO are pathologies that could lead to early changes to the vascular structure and function, and may, in turn, be etiologic to numerous complications like macular edema, retinal ischemia and optic neuropathy. For these cases, quantification and visualization of vasculature, capillaries and flow can be a versatile diagnostic tool. For example, ischemic regions in the retina can be mapped to evaluate the extent of damage and further management of the disease. In addition to the vascular rich retina, there is a small area in the macula, at the fovea, which is devoid of any capillaries. This is called the Foveal Avascular zone (FAZ), and abnormal changes in the size of this region are also indicative of pathologies like ischemic maculopathy and DR. The quantification of the FAZ and measuring changes in its size over time can be a clinically significant numerical score for disease presence and progression, especially for DR.
Conventional techniques to visualize retinal vasculature are invasive in nature, and use pharmacological techniques to modify contrast in the imaged retina. Contemporary clinical practice involves injection of a fluorescent dye (such as fluorescein or indocyanine green (ICG)) into the systemic circulation, and the eye is then scanned to generate an image, which selectively shows the path of the dye through the vascular network (FA, Fluorescein Angiography). No information of the depth structure of the vasculature is captured by this method. In contrast, vascular images generated by examining the OCT intensity or phase signal are non-invasive, and provide comparable fidelity in capturing the existing vascular network with blood flow contrast along with its depth encoding.
There have been a few descriptions detailing the detection of FAZ in contemporary literature, but all the methods discussed are either manual, performed by experts, or are semi-automated, requiring an informed bootstrapping of the downstream method with a manually selected starting point or region. A few relevant publications, and references therein, serve to inform about the existing prior art (see for example Kim et al. “Noninvasive Imaging of the Foveal Avascular Zone with High-Speed, Phase-Variance Optical Coherence Tomography” Investigative Ophthalmology & Visual Science, 53 (1), 85-92 (2012), Zheng et al. “Automated segmentation of foveal avascular zone in fundus fluorescein angiography” Retina. 51(7): 3653-3659 (2010), Yong et al. “Novel Noninvasive Detection of the Fovea Avascular Zone Using Confocal Red-Free Imaging in Diabetic Retinopathy and Retinal Vein Occlusion” Retina. 52: 2649-2655 (2011), and Wang et al. “Imaging Retinal Capillaries Using Ultrahigh-Resolution Optical Coherence Tomography and Adaptive Optics” Invest. Ophthalmol. Vis. Sci. 52. 6292-6299 (2011) hereby incorporated by reference).
Diagnostically, changes to both the vascular and the typically avascular retina are important indicators of developing retinal pathologies. Although visualization of the vascular structure helps in boosting the diagnostic efficacy of this imaging technique, it can be further leveraged by augmenting the visualization with some salient quantifications and metrics derived from the identified vascular and avascular sections of the retina. The primary quantity of interest is the global or structure-specific retinal blood flow kinetics, which can be challenging to quantify because of low flow velocities relative to the temporal resolution of the technique, and the almost perpendicular orientation of the capillaries with respect to the probing beam. In addition to visualization, derived quantifiers from the angiography data which serve to aid in differentiating capillary networks in healthy and diseased eyes are also desirable.
Recently, a few research groups have explored quantitative methods for angiography data to construct meaningful numerical indicators of vascular pathology. Techniques such as fractal dimension analysis have been used to study vessel morphology, distribution and allied features. Avakian et al. demonstrated the use of fractal characterization of fluorescein angiography (FA) images of the human retina to distinguish between healthy and diseased retina (see for example Avakian, et al., “Fractal analysis of region-based vascular change in the normal and non-proliferative diabetic retina,” Curr. Eye Res. 24, 274-280, 2002). Schmoll et al. applied a related fractal dimension algorithm to analyze the integrity of the parafoveal capillary network non-invasively using OCT angiography images (see for example Schmoll et al. “Imaging of the parafoveal capillary network and its integrity analysis using fractal dimension” Biomed. Opt. Express 2, 1159-1168, 2011). Also, Jia et al. and An et al. applied simpler vessel density measurements to quantitatively evaluate the capillary network within the human optic nerve head using OCT angiography methods (see for example Jia et al., “Quantitative OCT angiography of optic nerve head blood flow,” Biomed. Opt. Express 3, 3127-3137, 2012 and An et al., “Optical microangiography provides correlation between microstructure and microvasculature of optic nerve head in human subjects,” J. Biomed. Opt. 17, 116018, 2012).
One piece of important anatomical information that is captured by OCT angiography is the depth information, or the spatial distribution of the vessels in the retinal tissue. To visualize the complex capillary networks and to make use of the additional depth information gained by OCT angiography compared to traditional angiography methods such as FA, OCT angiography data is often displayed as 2D projections with the color encoded depth information (see Kim et al. “In vivo volumetric imaging of human retinal circulation with phase variance OCT,” Biomedical Optics Express, 2(6), 1504-1513 (2011)). Such 2D projections at least allow distinguishing capillary layers of different depths. They however lack the 3D impression and also don't provide easily accessible information of which larger retinal vessels feed and drain different capillary network regions.
Retinal vessel connectivity measures are also known for fundus photography, they however only focus on a few major retinal vessels in 2D fundus images, rather than visualizing the supply of dense, complex parafoveal capillary networks (see for example Al-Diri et al. “Automated analysis of retinal vascular network connectivity,” Computerized Medical Imaging and Graphics, 34, 462-470 (2010)). Ganesan et al. investigates the connectivity of vessels in mouse retinas from the largest vessels to the smallest capillaries in confocal microscopy images in order to develop a network model (see for example Ganesan et al. “Development of an Image-Based Network Model of Retinal Vasculature,” Annals of Biomedical Engineering 38(4) 1566-1585 (2010)). They however don't describe using this as a way to interactively visualize human angiography acquisitions.